Group acceptance sampling plan for exponential logarithmic distribution: an application to medical and engineering data

Abstract

In this study, we present a group-acceptance sampling plan (GASP) in which the lifetime of an item follows a two-parameter exponential logarithmic (EL) distribution. The lower and upper quantile values were used as quality indices for various constraint design parameters, including acceptance number, predefined consumer risk, minimum group size and test termination. However, the results show that the lower quantile values perform better than the upper quantile values in terms of saving time and cost with minimum groups (g) and acceptance numbers (c). We used two real-life datasets to estimate the unknown parameters using the maximum-likelihood method. A comparison was made between the GASP and the ordinary sampling plan (OSP) to check the efficiency of the sampling plan.